U.S. patent number 6,668,181 [Application Number 10/161,197] was granted by the patent office on 2003-12-23 for method for quantification of stratum corneum hydration using diffuse reflectance spectroscopy.
This patent grant is currently assigned to Sensys Medical, Inc.. Invention is credited to Thomas Blank, Frank Grochocki, Ken Meissner, Stephen L. Monfre, Jessica Rennert, Timothy L. Ruchti, Brian J. Wenzel.
United States Patent |
6,668,181 |
Wenzel , et al. |
December 23, 2003 |
Method for quantification of stratum corneum hydration using
diffuse reflectance spectroscopy
Abstract
An apparatus and method for non-invasively quantifying the
hydration of the stratum corneum of a living subject utilizes in
vivo spectral measurements made by irradiating skin tissue with
near infrared (NIR) light. The apparatus includes a spectroscopic
instrument in conjunction with a subject interface. The resulting
NIR absorption spectra are passed to an analyzer for further
processing, which includes detecting and eliminating invalid
spectral measurements, and preprocessing to increase the
signal-to-noise ratio. Finally, a calibration model developed from
an exemplary set of measurements is applied to predict the SC
hydration for the sample. The method of SC hydration measurement
provides additional information about primary sources of systematic
tissue variability, namely, the water content of the epidermal
layer of skin and the penetration depth of the incident light. The
stratum corneum hydration measurement is therefore suitable for
further spectral analysis and the quantification of biological and
chemical compounds, such as blood analytes.
Inventors: |
Wenzel; Brian J. (Cave Creek,
AZ), Monfre; Stephen L. (Gilbert, AZ), Ruchti; Timothy
L. (Gilbert, AZ), Meissner; Ken (Gilbert, AZ),
Grochocki; Frank (Chandler, AZ), Blank; Thomas
(Chandler, AZ), Rennert; Jessica (Scottsdale, AZ) |
Assignee: |
Sensys Medical, Inc. (Chandler,
AZ)
|
Family
ID: |
24687704 |
Appl.
No.: |
10/161,197 |
Filed: |
May 31, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
669781 |
Sep 25, 2000 |
6442408 |
|
|
|
Current U.S.
Class: |
600/310;
250/339.1; 250/341.8; 600/473 |
Current CPC
Class: |
A61B
5/0075 (20130101); A61B 5/14532 (20130101); A61B
5/1455 (20130101); A61B 5/1495 (20130101); A61B
5/441 (20130101); G01N 21/274 (20130101); G01N
21/359 (20130101); G01N 21/4785 (20130101); G01N
21/49 (20130101); A61B 5/1075 (20130101); A61B
5/7264 (20130101); A61B 2560/0223 (20130101); A61B
2560/0233 (20130101) |
Current International
Class: |
A61B
5/103 (20060101); G01N 21/47 (20060101); G01N
21/31 (20060101); G01N 21/49 (20060101); G01N
21/35 (20060101); A61B 005/00 () |
Field of
Search: |
;600/310,322,473,475
;356/303
;250/339.01,339.07,339.09,339.1,339.11,340,341.5,341.8 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Primary Examiner: Winakur; Eric F.
Attorney, Agent or Firm: Glenn Patent Group Glenn; Michael
A. Peil; Christopher
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATION
This application is a Continuation of U.S. patent application Ser.
No. 09/669,781, filed on Sep. 25, 2000, now U.S. Pat. No. 6,442,408
B1.
Claims
What is claimed is:
1. A method for quantifying hydration of living tissue
non-invasively, based on in vivo spectral measurements, comprising
the steps of: providing a calibration set of exemplary samples and
independent measurements, wherein a prediction model is developed
from said calibration set; measuring a spectrum at a selected
tissue measurement site on a living subject; estimating hydration
of said tissue measurement site from said spectrum according to
said prediction model.
2. The method of claim 1, wherein said spectral measurements
comprise NIR spectra.
3. The method of claim 1, wherein said measurements are made using
any of transmissive, diffuse reflectance and other methods, and
wherein parameters for said method are determined based on
information provided by said calibration set.
4. The method of claim 1, wherein said spectrum is denoted by a
vector m.epsilon.{character pullout}.sup.N of absorbance values
pertaining to a set of N wavelengths .lambda..epsilon.{character
pullout}.sup.N spanning a wavelength region of approximately 700 to
2500 nm.
5. The method of claim 1, further comprising the step of: detecting
outliers, wherein said outliers comprise invalid measurements.
6. The method of claim 5, wherein said invalid measurements result
from any of instrument problems, poor sampling technique, and
subjects outside of the calibration data set.
7. The method of claim 5, wherein said outlier detection step
comprises the steps of: performing a principal components analysis
(PCA) of said measured spectrum; and analyzing resulting
residuals.
8. The method of claim 7, wherein said PCA comprises projecting
said measured spectrum m onto five eigenvectors contained in a
matrix o that were previously developed through a PCA of absorbance
spectra from said exemplary data set, wherein the calculation is
given by: ##EQU6## wherein a one by five vector of scores is
produced, xpc.sub.0, where o.sub.k is the k.sup.th column of the
matrix o.
9. The method of claim 8, wherein residual, q, is determined
according to:
10. The method of claim 1, further comprising the step of
preprocessing said measured spectrum.
11. The method of claim 10, said preprocessing step including
transformations that attenuate noise and instrumental
variation.
12. The method of claim 11, wherein said preprocessing
transformations include any of: scaling, normalization, smoothing,
and filtering.
13. The method of claim 11, wherein a preprocessed measurement is
determined according to:
14. The method of claim 1, wherein said estimating step includes
any of multiple least squares regression (MLR), principle component
regression (PCR), and partial least squares regression (PLR)
analysis wherein the measurement y.epsilon.{character
pullout}.sup.N is processed according to:
15. The method of claim 1, wherein said step of measuring a
spectrum at a selected tissue measurement site on a living subject
comprises: using an apparatus for measuring an NIR absorbance
spectrum to measure said spectrum.
16. The method of claim 15, wherein said apparatus comprises an
energy source, said energy source comprising a plurality of LED's,
each of said LED's emitting energy at a different targeted
wavelength within a wavelength range of approximately 700-2500 nm;
a sample probe head, said sample probe head comprising a subject
interface and being substantially in contact with said tissue
measurement site and delivering NIR energy emitted by said energy
source to said tissue measurement site; and a reference probe head,
said reference probe head delivering NIR energy emitted by said
energy source to an internal reference standard having known
spectral characteristics.
17. The method of claim 16, wherein said tissue measurement site
comprises an area of the skin of said living subject.
18. The method of claim 17, wherein the absorbance spectrum is
calculated by: ##EQU7## where m is the absorbance spectrum, and R
is an intensity signal from said sample probe head, R.sub.0 is an
intensity signal from said reference probe head.
19. The method of claim 18, wherein said spectrum, m, is analyzed
to detect outliers, said outliers comprising invalid measurements,
or readings outside the range of said prediction model.
20. The method of claim 19, wherein said spectrum is preprocessed,
said preprocessing step comprising: multiplicative scatter
correction (MSC), wherein said spectrum is processed through a
rotation that fits it to a reference spectrum .sup.m determined
from said calibration set; and mean centering.
21. The method of claim 20, wherein said MSC step comprises the
steps of: fitting said spectrum via linear regression according
to:
22. The method of claim 21, wherein said mean centering step
comprises the steps of: calculating a mean for each LED absorbance
from said calibration set; subtracting said mean from each LED
absorbance in the measured spectrum.
23. The method of claim 17, wherein said prediction model is a
multiple linear regression (MLR) model for predicting Stratum
Corneum hydration.
24. The method of claim 23, wherein the prediction calculation is
given by: ##EQU9## where {character pullout} is the predicted
hydration; x.sub.1, x.sub.2, and x.sub.3 are the absorbance of each
LED, .sub.13 1, .sub.13 2, and .sub.--3 are the coefficients to the
absorbance of each LED, and_ is the error associated with the
model.
25. The method of claim 24, wherein the coefficients are calculated
by:
26. The method of claim 1, wherein said prediction model includes a
preprocessing algorithm.
27. The method of claim 1, further comprising the step of
developing said prediction model using factor-based analytical
methods, wherein a set of abstract features is developed that is
capable of representing spectral variation related to tissue
hydration.
28. The method of claim 27, wherein said developing step comprises
the steps of: providing NIR absorbance spectra; selecting
wavelengths from said spectra, wherein said spectra are sub-divided
into one or more regions according to wavelength; preprocessing and
normalizing said spectra, wherein spectral variation related to
tissue hydration is enhanced; projecting said measurements onto one
or more sets of previously determined factors, said factors
comprising eigenvectors, to determine scores, wherein said scores
constitute extracted features; and subjecting said scores to a
prediction procedure, said procedure comprising any of linear
discriminant analysis, SIMCA, k nearest neighbor, fuzzy
classification, an artificial neural networks.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention relates to the use of spectroscopy to characterize
living tissue. More particularly, the invention relates to an
apparatus and method for quantifying hydration of the stratum
corneum of a living subject non-invasively, based on irradiation of
the skin tissue with near infrared light energy.
2. Description of Related Art
Near infrared (NIR) tissue spectroscopy is a promising non-invasive
technology that bases measurements on the irradiation of a tissue
site with NIR energy in the 700-2500 nanometer wavelength range.
The energy is focused onto an area of the skin and propagates
according to the scattering and absorption properties of the skin
tissue. Therefore, the reflected or transmitted energy that escapes
and is detected provides information about the tissue volume that
is encountered. Specifically, the attenuation of the light energy
at each wavelength is a function of the structural properties and
chemical composition of the tissue. Tissue layers, each containing
a unique heterogeneous particulate distribution, affect light
absorbance through scattering. Chemical components such as water,
protein, fat and blood analytes absorb light proportionally to
their concentration through unique absorption profiles or
signatures. The measurement of tissue properties, characteristics
or composition is based on detecting the magnitude of light
attenuation resulting from its respective scattering and/or
absorption properties.
Stratum Corneum Hydration Measurement
The quantification of hydration of the stratum corneum has
commercial benefits in certain industries for monitoring skin
condition and for attaining a better understanding of how hydration
affects the stratum corneum. The current method of measuring the
hydration of the stratum non-invasively is based on the electrical
characteristics of the stratum corneum. The technology measures the
capacitance, admittance, impedance, or susceptance of the stratum
corneum.
Spectroscopic approaches to measuring hydration of the stratum
corneum have been explored. See, for example, R. Potts, D. Guzek,
R. Harris, J. McKie, A Noninvasive, In Vivo Technique to
Quantitatively Measure Water Concentration of the Stratum Corneum
Using Attenuated Total-Reflectance Infrared Spectroscopy, Archives
of Dermatological Research, Springer-Verlag, Vol. 277, (1985).
Potts, et al. performed a variety of in vitro experiments using
Attenuated Total Reflectance (ATR) spectroscopy in the infrared
region of light, and determined that hydration of the skin was
highly correlated (0.99) to the ambient humidity. He developed a
variety of preprocessing techniques like the protein ratio and the
moisture factor to measure the hydration of the stratum. He
concluded that water content in the stratum corneum could be
measured in vitro using ATR infrared spectroscopy. The Potts
teachings however are directed to an in vitro method and are
therefore unsuited to non-invasive, in vivo measurements.
Martin did a series of experiments related to in vivo measurement
using diffuse reflectance near infrared spectroscopy. See K.
Martin, Direct Measurement of Moisture in Skin by NIR Spectroscopy,
Journal of Society of Cosmetic Chemists, Vol. 44 (1993). Martin's
work lead to the finding that three different types of water may be
detected in the spectra of skin. The different types of water were
found in the combination region (1058-1950 nm) using the second
derivative of the spectrum; second derivative intensities were
found to correlate with ambient humidity levels. It was found that
the bulk water of the stratum corneum correlates most directly with
ambient humidity. Bulk water was water that mostly resembled that
of regular water and was not bound to any protein. It was also
found that the primary hydration water correlated the least with
ambient humidity.
Martin's further work investigated the use of measuring sites at a
variety of body locations having skin of varying thickness. See K.
Martin, In Vivo Measurements of Water in Skin by Near Infrared
Reflectance, Applied Spectroscopy, Vol. 52(7)(1998). While a higher
standard deviation was noted, the previous correlations with
different water types in the skin were confirmed. Additionally,
light scattering by the skin was found to decrease with increasing
hydration. The Martin teachings, however, do not address the
persistent problem in the art of compensating for structural and
physiological variation between individuals or variation over time
within the same individual.
Blood Analyte Prediction
While non-invasive prediction of blood analytes, such as blood
glucose concentration, has been pursued through NIR spectroscopy,
the reported success and product viability has been limited by the
lack of a system for compensating for structural variations between
individuals that produce dramatic changes in the optical properties
of the tissue sample. For example, see O. Khalil, Spectroscopic and
clinical aspects of non-invasive glucose measurements, Clin Chem.
Vol. 45, pp 165-77 (1999) or J. Roe, B. Smoller, Bloodless Glucose
Measurements, Critical Reviews in Therapeutic Drug Carrier Systems.
Vol. 15, no. 3, pp. 199-241, 1998. These differences are largely
anatomical and provide distinct systematic spectral absorbance
features or patterns that can be related directly to specific
characteristics such as dermal thickness, protein levels and
hydration. While the absorbance features are repeatable within a
subject, over a population of subjects they produce confounding
nonlinear spectral variation. Therefore, differences between
subjects are a significant obstacle to the non-invasive measurement
of blood analytes through NIR spectral absorbance.
The related U.S. Patent Application, S. Malin, T. Ruchti, An
intelligent system for noninvasive blood analyte prediction, U.S.
patent application Ser. No. 09/359,191 (Jul. 22, 1999) discloses an
apparatus and procedure for substantially reducing this problem by
classifying subjects according to major skin tissue characteristics
prior to blood analyte prediction. The selected characteristics are
representative of the actual tissue volume irradiated and the
amount of the target analyte that is sampled. By grouping
individuals according to the similarity of spectral characteristics
representing the tissue structure, the nonlinear variation
described above is reduced and prediction of blood analytes becomes
more accurate.
SUMMARY OF THE INVENTION
The present invention provides a novel apparatus and related
procedures for the quantification of hydration of the stratum
corneum through NIR tissue spectroscopy having particular benefit
in several areas, including tissue state evaluation and blood
analyte prediction. The invention utilizes NIR diffuse reflectance
to measure the hydration of the stratum corneum. A spectroscopic
apparatus in conjunction with an optical subject interface is used
to measure tissue properties and characteristics non-invasively
that are manifested spectrally and vary systematically according to
the hydration of the subject's stratum corneum.
The procedure for quantifying stratum corneum hydration involves a
calibration model that is empirically derived from a set of
exemplary samples consisting of NIR tissue measurements and
corresponding independent measurements made with a corneometer. The
model is a set of parameters and computer generated code that is
implemented to predict the hydration of the subject's stratum
corneum. The general procedure involves the steps of taking
spectral measurements, typically in the near IR region of 700 to
2500 nm; detecting outliers, invalid measurements resulting from
poor sampling technique, or instrument problems, or a subject
outside of the calibration set; preprocessing, in which the
spectral measurements are subjected to various operations that
attenuate noise and instrumental variation; and prediction, in
which the previously mentioned calibration model is applied to
arrive at an estimation of the hydration of the subject's stratum
corneum.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 provides a block diagram of a system for predicting stratum
corneum hydration, according to the invention;
FIG. 2 illustrates a typical non-invasive NIR absorbance
spectrum;
FIG. 3 provides a block diagram of a hydration meter, according to
the invention;
FIG. 4 illustrates an arrangement of illumination and detection
fibers in the hydration meter of FIG. 3, according to the
invention;
FIG. 5 illustrates an arrangement of illumination and detection
fibers in a sample probe head of the hydration meter of FIG. 3,
according to the invention;
FIG. 6 illustrates an arrangement of illumination and detection
fibers in a reference probe head of the hydration meter of FIG. 3,
according to the invention;
FIG. 7 shows a plot of actual SC hydration measurements vs.
predictions in a calibration model for predicting SC hydration,
according to the invention; and
FIGS. 8 and 9 show plots of actual SC hydration measurements vs.
predicted for two different subjects, based on the calibration
model of FIG. 7, according to the invention.
DETAILED DESCRIPTION
The system for quantifying the Stratum Corneum hydration
non-invasively provides an apparatus for measuring the near
infrared absorption by tissue irradiated with near infrared energy
and a procedure for determining hydration of the Stratum Corneum.
Although the invented apparatus and procedure are described herein
with respect to quantifying hydration of the Stratum Corneum, one
skilled in the art will recognize that the invention has
application in quantifying hydration of other forms of tissue.
Hydration Prediction Apparatus
The apparatus includes an energy source 11, one or more sensor
elements, an interface 12 to the subject 10, a wavelength selection
device and an analyzer. The source generates and transmits
near-infrared energy in the wavelength range 700-2500 nanometers
and consists of a device such as an LED array 11 or a quartz
halogen lamp. The sensing elements are detectors 14, which are
responsive to the targeted wavelengths. The method of wavelength
separation includes a monochromator, an interferometer or
successive illumination through the elements of an LED array 11.
The interface to the subject comprises a means of transmitting
energy from the source 11 to the target skin tissue measurement
site and includes, for example a light pipe, fiber-optic probes, a
lens system or a light directing mirror system. Energy is collected
from the surrounding tissue areas in reflectance mode at an
optimally determined distance(s) through the use of detectors 13 or
fiber optics. Alternately, energy is collected in a transmission
mode through a skin fold, ear lobe, finger or other extremity. The
collected light is converted to a voltage 14 and sampled through an
analog-to-digital converter 15 for analysis on a data processing
system.
In the preferred embodiment, a group of LED's 11 is employed to
transmit energy of pre-selected wavelengths to the skin; the LED's
are radially surrounded by detection fibers 13 at specific
distances. The LED's are alternately energized and the detected
energy of each LED reflected or transmitted through the skin is
used to form one spectrum. The edge-to-edge distance between each
of the LED's and the detector elements, or the distance between the
point of illumination, comprising the light-emitting surface of the
LED's, and the point of detection is a minimum of 40 .mu.m and a
maximum of 1 mm. Distances of less than 40 .mu.m produce too much
surface reflection of the NIR radiation and distances of greater
than 1 mm result in too much penetration of the NIR radiation. The
set of wavelengths includes 1070, 1180, 1280 nm and 1110, 1190, and
1280 nm. However, other wavelength ranges, corresponding to water
bands in the NIR absorbance spectrum are also suitable. Coupling of
the illumination and detector elements, shown in detail in FIG. 4,
is accomplished through fiber optics. One skilled in the art will
appreciate that other coupling methods are suitable, including
optics and lens systems, subject to the criterion for the distances
between the point of illumination and detection. The detected
intensity from the sample is converted to a voltage through analog
electronics 14 and digitized through a 16-bit A to D converter
(ADC) 15. The spectrum is passed to the hydration prediction
procedure 16 for processing. First, the absorbance is calculated 17
on the basis of the detected light through--log(R/R.sub.0) where R
is the reflected light and R.sub.0 is the light incident on the
sample determined by scanning a reference standard. Subsequent
processing steps, described below, result in either a hydration
prediction or a message indicating an invalid scan. A block diagram
of the integrated system is shown in FIG. 1.
Alternately, the measurement can be accomplished with existing NIR
spectrometers that are commercially available, including a Perstorp
Analytical NIRS 5000 spectrometer or a Nicolet Magna-IR 760
spectrometer. In addition, the measurement can be made by
collecting reflected light off the surface of the skin or light
transmitted through a portion of the skin, such as the finger or
the ear lobe. Further, the use of reflectance or transmittance can
replace the preferred absorbance measurement.
Hydration Prediction Procedure
The general procedure for quantifying hydration based on the
measured spectrum, shown in FIG. 1, is implemented in a data
processing system such as a microcomputer 44 that automatically
receives the measurement information from the ADC 15. The hydration
quantifying procedure comprises a series of steps, including
outlier detection 18, preprocessing 19, and hydration prediction 20
wherein each step is a procedure in itself. Each procedure relies
on a calibration set of exemplary measurements. Herein below, the
general steps of the Hydration Prediction Procedure are summarized,
with a detailed description following in the subsequent section
titled "Implementation."
Measurement (17)
The measurement is a spectrum denoted by the vector
m.epsilon.{character pullout}.sup.N of absorbance values pertaining
to a set of N wavelengths .lambda..epsilon.{character
pullout}.sup.N that span the near infrared (700 to 2500 nm).
Atypical plot 30 of m versus .lambda. is shown in FIG. 2.
Outlier Detection (18)
The outlier detection procedure provides a method of detecting
invalid measurements through spectral variations that result from
problems in the instrument, poor sampling of the subject or a
subject outside the calibration set. The preferred method for the
detection of spectral outliers is through a principal component
analysis and an analysis of the residuals. See H. Martens, T. Naes,
Multivariate Calibration, John Wiley & Sons, New York (1989).
First, the spectrum, m, is projected onto five eigenvectors,
contained in the matrix o, that were previously developed through a
principal components analysis (on a calibration set of exemplary
absorbance spectra) and are stored in the computer system of the
device. The calculation is given by ##EQU1##
and produces the 1 by 5 vector of scores, xpc.sub.0, where o.sup.k
is the k.sup.th column of the matrix o. The residual, q, is
determined according to
and compared to three times the standard deviation of the expected
residual (of the calibration set). If greater, the sample is
reported to be an outlier and the hydration measurement procedure
is terminated.
Preprocessing (19)
Preprocessing includes operations such as scaling, normalization
smoothing, derivatives, filtering and other transformations that
attenuate the noise and instrumental variation without affecting
the signal of interest. The preprocessed measurement,
x.epsilon.{character pullout}.sup.N, is determined according to
where h: {character pullout}.sup.N.times.2.fwdarw.{character
pullout}.sup.N is the preprocessing function.
Prediction (20)
Prediction may include operations such as multiple linear least
squares regression (MLR), principle component regression (PCR), and
partial least squares regression (PLR) analysis that process the
measurement, y.epsilon.{character pullout}.sup.N, according to
where g: {character pullout}.sup.N.fwdarw.{character pullout}.sup.1
is the regression function.
Implementation Details
This section describes a particular embodiment of the apparatus and
specific procedures for quantifying SC hydration. The structure of
the procedures relies on a priori knowledge of the systematic
variation of the skin structure, namely, the hydration state of the
stratum corneum and the variation in path depth of the irradiated
light. However, the parameters of each procedure, such as the
eigenvectors for outlier detection, are determined on the basis of
an experimental data set providing exemplary information.
Apparatus
FIG. 3 provides a block diagram for the hydration meter 40. The
light source 11 for this device includes an array 11a of three
light emitting diodes (LED's). The current source for the LED's is
an LED driver 41 connected to a power supply 42 that pulses the
LED's at a frequency of between 1 kHz and 10 kHz. The LED driver 41
supplies a current of up to 3.0 amperes. The LED's used for this
device have a peak wavelength at 1.07 Im, 1.22 Im, and 1.25 Im.
Each LED is equipped with a bandpass interference filter 11b; the
bandpass interference filters of the preferred embodiment have
center wavelengths of 1080 nm, 1180 nm, and 1280 nm, respectively,
with their full width half maximum ranging from 11.0 to 14.8 nm.
The light is transmitted to the probe heads 45, 46 via fiber optics
51a-c, 52a-c. FIG. 4 illustrates the coupling of the LED's 11a with
the probe heads 45, 46 by means of fiber optics 51a-c, 52a-c.
Each LED has seven 100 Im core diameter fiber optics associated
with it. Six of these fiber optics 51a-c go to the sample probe
head 45, and one 52a-c goes to the reference probe head 46. The
sample probe head 45 is the subject interface 12 of the device that
comes into contact with the stratum corneum. FIG. 5 shows a
preferred fiber optic arrangement for the sample probe head 45,
comprising a total of eighteen illuminating fibers 51a-c and
sixty-nine detecting fibers 51d. Each illuminating fiber 51a-c is
completely surrounded, in a closed, packed arrangement, by
detection fibers 51d for greatest light collection. Shown in FIG.
6, the reference probe head 46 is used to collect a dual beam
reference of an internal diffuse reflectance standard having known
spectral characteristics. The reference probe has a total of three
illuminating fibers 52a-c and from 20 to 30 detecting fibers 52d.
The diffuse reflected light from each of the probe heads, sample
and reference, travels via optical fibers 51d, 52d to an optical
system 53a, b that focuses the light onto the 1.9 Im InGaAs
detectors 13. The fiberoptics are coupled to the various components
with connecting elements 54a-f. In the preferred embodiment of the
invention, the connecting elements 54 are brass connectors, but
other equally suitable alternatives will be apparent to those
skilled in the art.
The signals from the detectors are amplified in the analog front
end 47 (AFE). The AFE also converts the current signal from the
detectors to a voltage signal before transmitting the signal to the
lock-in amplifier 48. The phase modulating lock-in amplifier 48
receives the signal from the AFE 47 and a reference signal from the
LED driver 41. The lock-in amplifier 48 amplifies signals that are
in phase with the reference signal. This increases the
signal-to-noise ratio, and gives a direct current output. The
output from the lock-in amplifier 48 goes through a 16-bit analog
to digital converter (ADC) 15.
A laptop computer 44 or other data processing device receives the
signal from the ADC 15, and predicts the hydration based on the
invented algorithm 16 described further below. After the signal is
processed, the prediction result is displayed on a display device
43 attached to the laptop 44 or other data processing device. The
laptop also controls the master sequence 49 on the LED's. The
laptop controls which LED is emitting and the time period for which
each LED is emitting.
SC Hydration Prediction
The preferred analytical method for hydration prediction according
to the invention is Multiple linear regression (MLR); the
prediction calculation is given by: ##EQU2##
where {character pullout} is the predicted hydration; x.sub.1,
x.sub.2, and x.sub.3 are the absorbance of each LED,
.multidot..sub.1, .multidot..sub.2, and .multidot..sub.3 are the
coefficients to the absorbance of each LED, and A is the error
associated with the model. The coefficients are calculated by
where x is the matrix of absorbance values after the preprocessing
techniques are complete, y is the corneometer readings for each
spectral measurement, and w is the matrix containing the
coefficients: ##EQU3##
Absorbance is calculated 17 by: ##EQU4##
where m is the absorbance spectrum, R is the intensity signal from
the sample probe head, and R.sub.0 is the intensity signal from the
reference probe head. The absorbance spectrum, m, is passed through
the outlier detection system 18 to remove any bad measurements or
readings outside the prediction model's range. After outlier
detection, the signal is preprocessed 19 to attenuate any noise and
instrumental variation. The preprocessing techniques employed are
multiplicative scatter correction and mean centering. The spectrum
is processed, using multivariate scatter correction through a
rotation that fits it to the expected or reference spectrum n,
determined from the calibration set. See P. Geladi, D. McDougall,
H. Martens, Linearization and Scatter-Correction for Near-infrared
Reflectance Spectra of Meat, Applied Spectroscopy, Vol. 39, pp.
491-500 (1985). First, the spectrum is fitted via linear regression
according to
where a and b are the slope and intercept and e is the error in the
fit. The spectrum is then corrected through: ##EQU5##
where x is the processed absorbance spectrum. From this spectrum,
the mean from an exemplary data set is calculated for each LED
absorbance. The mean is then subtracted from each LED absorbance in
the measured data set. After mean centering the data, it is passed
through the multiple linear regression model for the prediction of
SC hydration. For the current embodiment, the coefficients for the
multiple regression model, .multidot..sub.1, .multidot..sub.2, and
.multidot..sub.3, are 2411.4, -2486.6, and 257.2, respectively.
Other methods of developing a prediction model for the hydration of
the stratum corneum may be used, for example, using factor analysis
to develop a set of abstract features capable of representing the
spectral variation related to hydration. For factor analysis, the
spectral measurements, NIR absorbance spectra similar to that of
FIG. 2, are used. The spectrum is sub-divided into one or more
regions according to wavelength (wavelength selection) and is
preprocessed and normalized to enhance spectral variation related
to SC hydration. The measurements are projected onto one or more
sets of previously determined factors (eigenvectors) to determine
the scores. The scores constitute the extracted features and are
subjected to a prediction procedure, such as linear discriminate
analysis, SIMCA, k nearest-neighbor, fuzzy classification and
various forms of artificial neural networks to predict hydration of
the stratum corneum. See R. Duda, P. Hart, Pattern Classification
and Scene Analysis, John Wiley & Sons, New York (1973) or S.
Wold, M. Sjostrom, SIMCA: A method for analyzing chemical data in
terms of similarity and analogy, Chemometrics: Theory and
Application, ed. B. R. Kowalski, ACS Symposium Series, Vol. 52
(1977) or J. Bezdek, S. Pal, eds., Fuzzy Models for Pattern
Recognition, IEEE Press, Piscataway, N.J. (1992) or J. Keller, M.
Gray, J. Givens, A fuzzy k nearest neighbor algorithm, IEEE
Transactions on Systems, Man, and Cybernetics, Vol. SMC-15(4), pp.
580-585, (July/August, 1985) or Y. Pao, Adaptive Pattern
Recognition and Neural Networks, Addison-Wesley Publishing Company,
Reading, Mass. (1989).
Experimental Data Set
A study was performed to develop a model for predicting SC
hydration. The spectroscopic measurements were made using a
spectrometer instrument according to an embodiment of the
invention, comprising a quartz lamp, a monochromator, a fiber optic
probe, and a detector set-up. The study consisted of four human
subjects (3 males and 1 female), in which the hydration of the SC
at the measurement site was modified by occluding the skin.
Different occlusion times were employed to develop a range of
hydration values, with no treatment of the skin at the sampling
site prior to measurement. Stratum corneum hydration was measured
independently by the corneometer CM 825, produced by Courage &
Khazaka of Cologne, Germany. Each subject had a minimum of eight
spectral scans with corresponding corneometer readings over a
period of at least two days in duration, each scan constituting a
sample. The spectral measurements and the corresponding corneometer
readings are referred to as the "Experimental Data Set" herein
below.
Feasibility
To demonstrate feasibility of the invented apparatus and method,
the Experimental Data Set was analyzed using the previously
described procedures. Outliers were removed using the outlier
detection procedure previously described. Subsequently the data
were preprocessed using MSC, followed by mean centering based on
the mean of the emitting region of the filters based on their full
width half maximum characteristics. The regions used were
1073-1087, 1175-1185, and 1275-1285 nm. Finally, MLR was applied to
the data set. The calibration model was first developed using the
samples of all four subjects, and subsequently validated using a
"leave five out" cross-validation strategy. FIG. 6 shows a plot of
actual corneometer measurements vs. predictions for the entire
experimental data set. The standard error of prediction (SEP) for
the experimental data set was 3.6995. Subsequently, a calibration
model was developed and validated by using three subjects to
develop the calibration model, and using the resulting model to
predict SC hydration for the samples of the remaining subject.
FIGS. 8 and 9 show plots of actual corneometer measurements vs.
predictions for subjects four and three, respectively. The SEP was
4.2851 for subject four predictions and 6.1179 for subject three
measurements.
Although the invention as described herein above utilizes three
wavelength regions, one skilled in the art will recognize that that
a different number of wavelength regions and specific wavelengths
can be utilized, depending upon the requirements of the
measurement. For example, an improvement in measurement accuracy
can be achieved through the addition of more wavelength regions in
the 1400-1500 nm and 1900-2500 nm regions.
Furthermore, the invention as described specifies particular
wavelength regions for the measurement of tissue hydration.
However, other spectral regions may be selected, in which
absorbance due to water is present. For example, tissue hydration
can be measured using ranges of 1400-1550 nm, 1720-1850 nm and
1900-2050 nm.
Finally, while the invention has been described in relation to the
Stratum Corneum, hydration of the epidermis, the dermis and deeper
tissue regions can be measured in the same manner as described by
adjusting the illumination-to-detection distance according to the
targeted depth of penetration.
Although the invention has been described herein with reference to
certain preferred embodiments, one skilled in the art will readily
appreciate that other applications may be substituted for those set
forth herein without departing from the spirit and scope of the
present invention. Accordingly, the invention should only be
limited by the Claims included below.
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